Evaluation and mitigation of domain shift impact between volumetric submicro-scale and micro-scale computed tomography systems in the context of automated binary wood classification

Rapid and reliable automated identification of wood species can be a boon for applications across wood scientific context including forestry and biodiversity conservation, as well as in an industrial context via requirements for timber trade regulations. However, robust machine learning classifiers must be properly analyzed and immunized against domain shift effects. These can degrade the automated system performance for input data variations occurring in many scenarios. This work analyses the domain shift generated by using two differing sub-micro-scale and micro-scale computed tomography setups in the context of deep learning based binary wood classification from volumetric image data. Further, we examine several mitigation strategies and propose data- and model-level intertwined strategies to effectively minimize the performance domain gap. Core elements of the strategy include the combined usage of phase-correction methods, low-pass pyramid representation of the data and model normalization and regularization approaches. Vanishing domain performance differences led to the conclusion that the combined strategy ultimately prompted the model to learn robust features. These features are discriminative for input data from both sub-micro-system and micro-system domains, despite the substantial differences in data acquisition setup that propagate into fundamental image quality metrics like resolution, contrast and signal-to-noise ratio.